The Self-Organized Gene (Part 1)
Last year saw the 30th anniversary of Richard Dawkins' famous book, The Selfish Gene, the book that presented gene-centric evolution to a greater public. Controversial at the time, it is today a widely accepted theory that covers the connection between genetics and evolution through natural selection.
Dawkins' selfish gene should have the emphasis on gene - not selfish - as the primary point is that the gene is the basic unit that evolution through natural selection operates on. The selfish part is directly related to natural selection - genes that maximize their survival probabilities (which they do among other things through cooperation with other genes) live on in the gene pool while those less fit go extinct.
The title of this tutorial should be read in a different way: The Self-Organized Gene - emphasis on the self organized part. We are not going to be discussing the properties of the gene itself, but how gene functions can be analyzed using an adaptive method called self-organization. Our basic unit of operation won't be the gene, but the artificial neuron. In a way, there is a connection to the selfish part as well. While our units do not fall victim to natural selection, do not mutate and are not replicated, they do compete and interact which gives rise to the emergent global property of self-organization.
In more practical terms, in the tutorial we are going to explore unsupervised clustering of data. We will apply this to DNA microarrays, an exciting new technology that allows for very rapid expression profiling. We see how using adaptive self-organizing methods we can detect patterns in microarray data that can be used for understanding, detecting and fighting diseases caused by genetic factors.
In the first part of this tutorial we shall familiarize ourselves with the basic concepts of unsupervised learning, competitive learning and self organization. We shall also explore the self-organizing map as a powerful visualization tool and we'll take a look at a few simple examples to illustrate the principles.
In the second part of the tutorial we will cover meta-clustering before we move on to our target: the analysis of DNA microarray data. Once we have done that we will see how we can change our unsupervised clustering system into a supervised classification system in general and specifically in Synapse. Read more…